Thanks,
I tried the a couple of them before, but only to find differntiallly expressed genes between only two samples (x1.1, x1.2, y1.1, y1.2)
Now the case is a bit more complicated, i want it as i described above to find those DEG is a group of samples that is not expressed in the remaining. If any of these packages provide this can you please let me know which one and if you have links to any examples that looks like mine I would really appreciate that.

As far as I know, nobody has yet released (I know of groups who are working on such approaches) a package to deal with data in that fashion. Although if anyone here knows of one, I would also be interested in knowing.

Otherwise, as far as I know, all approaches require a pairwise comparison between different treatments.

So the approach I would use would be to do the pairwise comparisons and then filter accordingly.

So find your differentially expressed genes between x-y, x-z, y-z then find genes that are differentially expressed in both x-y and x-z and filter out genes that are differentially expressed in y-z. You should be left with a genes that are only differentially expressed between x and both y and z conditions, but which are unchanged in between your y and z conditions.

We actually used a similar approach to this when comparing transcriptomes between 3 different varieties of the same species, but looking for differences in only one of the conditions that could explain a phenotype.

Or do I have first to find out those differentially expressed between x conditions:
(x1.1,x1.2) vs (x2.1 vs x2.2)
(x1.1,x1.2) vs (x3.1 vs x3.2)
(x2.1 vs x2.2) vs (x3.1 vs x3.2)
and then the same for y conditions
(y1.1, y1.2) vs (y2.1, y2.2).

Up to my knowledge i can only use the packages above with only two conditions at a time
(x1.1,x1.2) vs (x2.1 vs x2.2)

I am not sure if there is a way to do two samples with more than one condition at the same time, as follow,
(x1.1, x1.2, x2.1, x2.2, x3.1, x3.2) vs (y1.1, y1.2, y2.1, y2.2) ???

Or do I have first to find out those differentially expressed between x conditions:
(x1.1,x1.2) vs (x2.1 vs x2.2)
(x1.1,x1.2) vs (x3.1 vs x3.2)
(x2.1 vs x2.2) vs (x3.1 vs x3.2)
and then the same for y conditions
(y1.1, y1.2) vs (y2.1, y2.2).

Up to my knowledge i can only use the packages above with only two conditions at a time
(x1.1,x1.2) vs (x2.1 vs x2.2)

I am not sure if there is a way to do two samples with more than one condition at the same time, as follow,
(x1.1, x1.2, x2.1, x2.2, x3.1, x3.2) vs (y1.1, y1.2, y2.1, y2.2) ???

Or that is considered same as my first question!!

Thanks

According to your opening post, all your x samples are biological reps, all your y samples are biological reps, and dido for all your z samples.

You NEED biological reps to do differential expression. So what I mean by doing pairwise comparisons of the conditions is all biological reps of x versus all biological reps of y and so on. There is no point in separating these replicates as they are what allows you to estimate the biological variance between samples.

Ah, this vastly complicates things. Can you clarify then exactly what your aim is. Since X1 is different from X2 are you interested only in a specific stage or a specific condition regardless of stage?